This invention relates to tool abnormality detecting methods and devices for detecting tool abnormalities in machine tools and more particularly to tool abnormality detecting methods and devices for detecting tool abnormalities by comparing input data which vary according to the tool conditions with a specified threshold value.
A method for detecting abnormalities of tools, such as cutting tools, has already been conceived in which input data varying according to tool conditions, such as a spindle current (the armature current of a field constant DC spindle motor for rotating the work), a feed current (the armature current of a field constant DC motor for feeding the tool), and vibration of the tool, are constantly monitored and abnormalities are found by comparing the above data with a preset threshold value.
However, the above method has an essential drawback that if the preset threshold value is not properly chosen, the tool is often judged normal when it is actually abnormal, and vice versa.
Many efforts have been made to solve this problem and various methods have been proposed as a result.
However, since the threshold value is affected by a variety of conditions, determining a proper threshold value prior to machining has been very difficult.
In addition, since a numerically-controlled machine tool (hereinafter referred to as an NC machine tool) generally performs versatile machinings, it is not sufficient to set only one threshold value. That is, since the threshold value is normally different from machining to machining, many threshold values should be set corresponding to individual machinings. Setting numbers of threshold values is extremely complex.
A particular problem here is that if the set threshold value is not properly chosen, the tool is judged normal when it is actually abnormal, and vice versa. However, it is in practice very difficult to determine all threshold values before machining, due to factors, such as the material of the object to be machined, involved.
Furthermore, the selection of one of many threshold values is performed based on a numerical control data (hereinafter referred to as an NC data), and if all different identification codes for discriminating and selecting many threshold values are to be inserted in the NC data, numbers of identification codes should be provided. In addition, since the number of usable identification codes when such arrangement is made is limited, changing the structure (organization) of the NC data to a great extent has become necessary.